Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for monitoring object motion in a venue, comprising: a transmitter located at a first position in the venue and configured for transmitting a first wireless signal through a wireless multipath channel, wherein the wireless multipath channel is impacted by a pseudo-periodic motion of an object in the venue; a receiver located at a second position in the venue and configured for: receiving a second wireless signal through the wireless multipath channel, wherein the second wireless signal is determined based on both the wireless multipath channel impacted by the pseudo-periodic motion of the object in the venue and the first wireless signal, and extracting at least one time series of channel information (CI) of the wireless multipath channel from the second wireless signal, wherein each CI comprises at least one of: a channel state information (CSI), a frequency domain CSI, a frequency domain CSI associated with at least one sub-band, a time domain CSI, a channel impulse response (CIR), a channel frequency response (CFR), a channel characteristics, and a channel filter response, of the wireless multipath channel; and a vital sign estimator configured for: determining that at least one portion of the at least one time series of CI (TSCI) in a current sliding time window is associated with the pseudo-periodic motion of the object in the venue, and computing a current characteristics related to the pseudo-periodic motion of the object in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, at least one portion of the at least one TSCI in a past sliding time window, and a past characteristics related to the pseudo-periodic motion of the object in the past sliding time window.
A system monitors an object's pseudo-periodic motion (like breathing) in a venue. It includes a transmitter at one location sending a wireless signal through a channel affected by the object's motion, and a receiver at another location obtaining a second signal influenced by this channel. The receiver extracts time-series channel information (e.g., Channel State Information or Channel Impulse Response) from the received signal. A vital sign estimator then identifies parts of this channel information within a current time window that relate to the object's motion. It calculates the motion's current characteristics (like frequency) based on channel information from the current window, past windows, or previously computed past characteristics.
2. The system of claim 1 , wherein: the object is at least one of: a life, a device, and a land; the pseudo-periodic motion represents at least one of the following of the object: breathing, heartbeat, periodic hand gesture, periodic gait, rotation, vibration, and earthquake; and the current characteristics represents a frequency of the pseudo-periodic motion.
A system monitors an object's pseudo-periodic motion (like breathing) in a venue. It includes a transmitter at one location sending a wireless signal through a channel affected by the object's motion, and a receiver at another location obtaining a second signal influenced by this channel. The receiver extracts time-series channel information (e.g., Channel State Information or Channel Impulse Response) from the received signal. A vital sign estimator then identifies parts of this channel information within a current time window that relate to the object's motion. It calculates the motion's current characteristics (like frequency) based on channel information from the current window, past windows, or previously computed past characteristics. In this system, the object can be a living being, a device, or even land. The pseudo-periodic motion can represent breathing, heartbeat, periodic hand gestures, gait, rotation, vibration, or an earthquake. The computed current characteristics specifically represent the frequency of this pseudo-periodic motion.
3. The system of claim 1 , wherein: the vital sign estimator is coupled to at least one of: the transmitter, the receiver, an additional transmitter, an additional receiver, a cloud server, a fog server, a local server, and an edge server; and at least one of the current characteristics and the past characteristics comprises information related to at least one of: a frequency of pseudo-periodic motion, a frequency characteristics, a frequency spectrum, a time period of pseudo periodic motion, a temporal characteristics, a temporal profile, a timing of pseudo-periodic motion; a starting time, an ending time, a duration, a history of motion, a motion type, a motion classification, a location of the object, a speed; a displacement, an acceleration, a rotational speed, a rotational characteristics, a gait cycle of the object, a transient behavior of the object, a transient motion, a change in pseudo-periodic motion, a change in frequency of pseudo-periodic motion, a change in gait cycle, an event associated with pseudo-periodic motion, an event associated with transient motion, a sudden-motion event, and a all-down event.
A system monitors an object's pseudo-periodic motion (like breathing) in a venue. It includes a transmitter at one location sending a wireless signal through a channel affected by the object's motion, and a receiver at another location obtaining a second signal influenced by this channel. The receiver extracts time-series channel information (e.g., Channel State Information or Channel Impulse Response) from the received signal. A vital sign estimator then identifies parts of this channel information within a current time window that relate to the object's motion. It calculates the motion's current characteristics based on channel information from the current window, past windows, or previously computed past characteristics. This vital sign estimator can be connected to the transmitter, receiver, additional transceivers, or various servers (cloud, fog, local, edge). The computed characteristics (current or past) can include details like frequency, time period, duration, motion history or type, location, speed, acceleration, rotational data, gait cycle, transient motion, changes in motion, or specific events like sudden movements or falls.
4. The system of claim 1 ; wherein the vital sign estimator comprises: a channel information processor, a spectral analyzer, and an energy spectrum normalizer that are configured for processing the at least one TSCI in the current sliding time window on both time domain and frequency domain; and a peak detector configured for detecting at least one peak in the frequency domain, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on the at least one peak in the frequency domain.
A system monitors an object's pseudo-periodic motion in a venue. It uses a transmitter and receiver to detect channel changes caused by motion, extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating its current characteristics using current or past channel data or prior characteristics. This estimator comprises a channel information processor, spectral analyzer, and energy spectrum normalizer, which process the time-series channel information in both time and frequency domains. A peak detector then identifies peaks in the frequency domain, and these frequency peaks are used to compute the motion's current characteristics.
5. The system of claim 1 , wherein the vital sign estimator comprises a channel information processor configured for, for each CI of a particular portion of the at least one TSCI in the current sliding time window: obtaining N1 frequency domain components of the CI; determining a timestamp associated with the CI; and preprocessing the N1 frequency domain components, wherein the preprocessing comprises: cleaning phases of the N1 frequency domain components, and normalizing the N1 frequency domain components such that the N1 frequency domain components have a unity total power, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on the preprocessed N1 frequency domain components of the CI of the particular portion of the at least one TSCI in the current sliding time window.
A system monitors an object's pseudo-periodic motion in a venue. It uses a transmitter and receiver to detect channel changes caused by motion, extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating motion characteristics based on current or past channel data or prior characteristics. Specifically, the vital sign estimator's channel information processor, for each channel information instance in a relevant portion of the current time series, obtains N1 frequency domain components, determines its timestamp, and preprocesses these components by cleaning their phases and normalizing them to have unity total power. The current motion characteristics are then computed using these preprocessed frequency domain components.
6. The system of claim 1 , wherein the vital sign estimator comprises a spectral analyzer configured for: for each CI of a particular portion of the at least one TSCI in the current sliding time window: converting N1 frequency domain components of the channel information using an inverse frequency transform to N2 time domain coefficients of the channel information, wherein: each channel information is associated with a timestamp, N2 is not smaller than N1, each of the N2 coefficients is associated with a time delay, and the inverse frequency transform comprises at least one of: inverse Fourier transform, inverse Laplace transform, inverse Hadamard transform, inverse Hilbert transform, inverse sine transform, inverse cosine transform, inverse triangular transform, inverse wavelet transform, inverse integer transform, inverse power-of-2 transform, combined zero padding and transform, and inverse Fourier transform with zero padding, and retaining first C of the N2 time domain coefficients, wherein C is not larger than N2; and correcting timestamps of all channel information of the particular portion of the at least one TSCI in the current sliding time window so that the corrected timestamps of time-corrected channel information are uniformly spaced in time, wherein correcting the timestamps comprises: identifying a particular CI with a particular timestamp to be replaced by a time-corrected CI with a corrected timestamp, and computing the time-corrected CI by computing the C retained time domain coefficients of the time-corrected CI at the corrected timestamp using an interpolation filter associated with the corrected timestamp, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on the C retained time domain coefficients of the CI of the particular portion of the at least one TSCI in the current sliding time window.
A system monitors an object's pseudo-periodic motion in a venue, detecting channel changes caused by motion via a transmitter and receiver, and extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating characteristics based on current/past channel data or prior characteristics. The estimator's spectral analyzer processes this. For each channel information instance, it converts N1 frequency components to N2 time domain coefficients (N2 >= N1) using an inverse frequency transform (e.g., inverse Fourier), retaining the first C coefficients (C <= N2). It then corrects all timestamps in that portion to be uniformly spaced, interpolating the retained C time domain coefficients for time-corrected channel information. The current motion characteristics are computed using these retained, time-corrected time domain coefficients.
7. The system of claim 6 , wherein the spectral analyzer is further configured for: for each of the C retained time domain coefficients: identifying a corresponding retained time domain coefficient of each CI of the particular portion of the at least one TSCI in the current sliding time window, applying bandpass filtering to all the corresponding identified retained time domain coefficients, and applying a frequency transform to all the corresponding identified retained time domain coefficients, wherein a length of the frequency transform is not smaller than a length of the portion, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on outputs of the frequency transform applied to each corresponding retained time domain coefficient of the CI of the particular portion of the at least one TSCI in the current sliding time window.
A system monitors an object's pseudo-periodic motion in a venue, detecting channel changes caused by motion via a transmitter and receiver, and extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating characteristics based on current/past channel data or prior characteristics. The estimator's spectral analyzer processes this. For each channel information instance, it converts N1 frequency components to N2 time domain coefficients (N2 >= N1) using an inverse frequency transform (e.g., inverse Fourier), retaining the first C coefficients (C <= N2). It then corrects all timestamps in that portion to be uniformly spaced, interpolating the retained C time domain coefficients for time-corrected channel information. For each of these C retained coefficients, the spectral analyzer applies bandpass filtering and a frequency transform (whose length is at least the portion length). The current characteristics are computed based on the outputs of this frequency transform.
8. The system of claim 7 , wherein the vital sign estimator further comprises an energy spectrum normalizer, and wherein the spectral analyzer and the energy spectrum normalizer are configured for: processing the outputs of the frequency transform applied to each corresponding retained time domain coefficient of the CI of the particular portion of the at least one TSCI in the current sliding time window, wherein the frequency transform comprises at least one of: Fourier transform, Laplace transform, Hadamard transform, Hilbert transform, sine transform, cosine transform, triangular transform, wavelet transform, integer transform, power-of-2 transform, combined zero padding and transform, and Fourier transform with zero padding, wherein the processing comprises at least one of: preprocessing, processing, post-processing, filtering, linear filtering, nonlinear filtering, folding, grouping, energy computation, low-pass filtering, bandpass filtering, high-pass filtering, matched filtering, enhancement, restoration, de-noising, spectral analysis, inverse linear transform, nonlinear transform, feature extraction, machine learning, recognition, labeling, training, clustering, grouping, sorting, thresholding, comparison with time-corrected channel information of another portion of another time series of channel information in another sliding time window, similarity score computation, vector quantization, compression, encryption, coding, storing, transmitting, representing, merging, combining, splitting; restricting to selected frequency band, spectrum folding, averaging, averaging over selected frequency, averaging over selected time domain coefficients, and averaging over antenna links.
A system monitors an object's pseudo-periodic motion in a venue, detecting channel changes caused by motion via a transmitter and receiver, and extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating characteristics based on current/past channel data or prior characteristics. The estimator's spectral analyzer processes this. For each channel information instance, it converts N1 frequency components to N2 time domain coefficients (N2 >= N1) using an inverse frequency transform (e.g., inverse Fourier), retaining the first C coefficients (C <= N2). It then corrects all timestamps in that portion to be uniformly spaced, interpolating the retained C time domain coefficients for time-corrected channel information. For each of these C retained coefficients, the spectral analyzer applies bandpass filtering and a frequency transform (e.g., Fourier). The vital sign estimator also includes an energy spectrum normalizer. Together, they process these frequency transform outputs through various operations like filtering, de-noising, spectral analysis, feature extraction, machine learning, thresholding, comparison, or merging and averaging across frequencies, time coefficients, or antenna links, to compute the final motion characteristics.
9. The system of claim 8 , wherein: the transmitter has at least one antenna; the receiver has at least one antenna; each of the at least one TSCI is associated with one of the at least one antenna of the transmitter and one of the at least one antenna of the receiver; the averaging over antenna links is weighted averaging over the at least one TSCI; and the averaging over selected time domain coefficients is weighted averaging over the C retained time domain coefficients.
A system monitors an object's pseudo-periodic motion in a venue, detecting channel changes caused by motion via a transmitter and receiver, and extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating characteristics based on current/past channel data or prior characteristics. The estimator's spectral analyzer processes this. For each channel information instance, it converts N1 frequency components to N2 time domain coefficients (N2 >= N1) using an inverse frequency transform (e.g., inverse Fourier), retaining the first C coefficients (C <= N2). It then corrects all timestamps in that portion to be uniformly spaced, interpolating the retained C time domain coefficients for time-corrected channel information. For each of these C retained coefficients, the spectral analyzer applies bandpass filtering and a frequency transform (e.g., Fourier). The vital sign estimator also includes an energy spectrum normalizer. Together, they process these frequency transform outputs through various operations like filtering, de-noising, spectral analysis, feature extraction, machine learning, thresholding, comparison, or merging and averaging across frequencies, time coefficients, or antenna links, to compute the final motion characteristics. Both the transmitter and receiver can have multiple antennas, with each time series of channel information corresponding to a specific transmitter-receiver antenna pair. The averaging over antenna links involves weighted averaging, and over selected time domain coefficients involves weighted averaging.
10. The system of claim 9 , wherein the vital sign estimator further comprises a peak detector configured for: identifying at least one local maximum and at least one local minimum the frequency domain; computing at least one local signal-to-noise-ratio-like (SNR-like) parameter for each pair of a local maximum and a local minimum adjacent to each other; identifying significant local peaks each being at least one of: a local maximum with SNR-like parameter greater than a first threshold T1 and a local maximum with an amplitude greater than a threshold T2.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector within the vital sign estimator identifies local maxima and minima in the frequency domain. For each adjacent maximum-minimum pair, it calculates a signal-to-noise-ratio-like parameter. Significant peaks, from which the motion characteristics are derived, are identified as local maxima where this SNR-like parameter exceeds a first threshold (T1) or where the amplitude surpasses a second threshold (T2).
11. The system of claim 10 , wherein: the at least one local maximum and the at least one local minimum are identified in the frequency domain using a peak detection approach.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector within the vital sign estimator identifies local maxima and minima in the frequency domain. For each adjacent maximum-minimum pair, it calculates a signal-to-noise-ratio-like parameter. Significant peaks, from which the motion characteristics are derived, are identified as local maxima where this SNR-like parameter exceeds a first threshold (T1) or where the amplitude surpasses a second threshold (T2). These local maxima and minima in the frequency domain are specifically identified using a designated peak detection algorithm.
12. The system of claim 10 , wherein the vital sign estimator further comprises a breathing rate estimator configured for: selecting a set of selected significant local peaks from the set of identified significant local peaks based on a selection criterion, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on the set of selected significant local peaks and frequency values associated with the set of selected significant local peaks.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). The vital sign estimator also includes a breathing rate estimator, which selects a subset of these identified significant local peaks based on specific criteria. The current motion characteristics are then computed using this selected set of significant local peaks and their associated frequency values.
13. The system of claim 12 , wherein the breathing rate estimator is further configured for: computing information associated with a remaining spectrum with the set of selected significant local peaks removed; and detecting an event associated with the current characteristics related to the pseudo-periodic motion of the object based on at least one of: a remaining spectral energy of the remaining spectrum, an adaptive threshold, and a finite state machine, where the event comprises at least one of: a presence, an absence, an appearance, a disappearance, a steady behavior and a non-steady behavior of at least one of: non-periodic motion, transient motion, strong motion, weak motion; strong background interference and another periodic motion.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). A breathing rate estimator then selects a subset of these peaks based on criteria to compute current motion characteristics from their frequencies. Additionally, the breathing rate estimator computes information about the remaining spectrum after the selected peaks are removed. It detects events related to the current motion characteristics, such as presence or changes in non-periodic motion, transient motion, strong/weak motion, strong interference, or other periodic motions. This event detection relies on the remaining spectral energy, an adaptive threshold, or a finite state machine.
14. The system of claim 13 , further comprising a system state controller configured for: computing adaptively at least one of: decision thresholds, threshold T1, threshold T2, lower bound associated with frequency of the selected significant local peaks, upper bound associated with frequency of the selected significant local peaks, search range, and a parameter associated with a selection of the selected significant local peaks, based on a finite state machine (FSM), wherein the FSM comprises at least one of: an Initiation (DIT) state, a Verification state, a PeakFound state, and a Motion state.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). A breathing rate estimator selects a subset of these peaks based on criteria to compute current motion characteristics. It also detects events related to motion or interference based on remaining spectral energy, adaptive thresholds, or a finite state machine. The system further includes a system state controller that adaptively computes various parameters, including decision thresholds, T1, T2, frequency bounds for selected peaks, and search ranges, all guided by a finite state machine (FSM). This FSM includes an Initiation (INIT) state, a Verification state, a PeakFound state, and a Motion state, managing the system's operational flow.
15. The system of claim 14 , wherein the system state controller is further configured for: entering the INIT state of the FSM in at least one predetermined way; computing the thresholds adaptively in a first way in the INIT state; detecting an event based on the thresholds; transitioning from the INIT state to a different state of the FSM based on at least one transition criterion; and computing the thresholds adaptively in a second way in the different state.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). A breathing rate estimator selects a subset of these peaks based on criteria to compute current motion characteristics. It also detects events related to motion or interference. The system further includes a system state controller that adaptively computes various parameters, including decision thresholds, T1, T2, and frequency bounds, all guided by a finite state machine (FSM) comprising INIT, Verification, PeakFound, and Motion states. The controller enters the INIT state in a predetermined manner, where it adaptively computes thresholds using a first method. It detects events based on these thresholds and transitions from the INIT state to a different FSM state based on specific criteria, then adaptively computes thresholds using a second method in that new state.
16. The system of claim 14 , wherein the system state controller is further configured for: entering the INIT state of the FSM; computing the thresholds adaptively in a first way; detecting an event based on the thresholds and at least one of: the set of selected significant local peaks and related characteristics; detecting excessive background interfering motion based on the thresholds and the remaining spectral energy; staying in the INIT state when the event is concluded to be “not detected” and the excessive background interfering motion is concluded to be “not detected;” transitioning from the INIT state to the Verification state when the event is concluded to be “detected” preliminarily and the detected event needs to be verified; and transitioning from the INIT state to the Motion state when the excessive background interfering motion is concluded to be “detected.”
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). A breathing rate estimator selects a subset of these peaks based on criteria to compute current motion characteristics. It also detects events related to motion or interference. The system further includes a system state controller that adaptively computes various parameters, including decision thresholds, T1, T2, and frequency bounds, all guided by a finite state machine (FSM) comprising INIT, Verification, PeakFound, and Motion states. In the INIT state, the controller adaptively computes thresholds using a first method. It detects an event based on these thresholds and selected peaks, and also detects excessive background interfering motion using thresholds and remaining spectral energy. The system remains in INIT if neither event nor interference is detected. It transitions to the Verification state if an event is preliminarily detected and needs verification, or to the Motion state if excessive background interference is detected.
17. The system of claim 16 , wherein the system state controller is further configured for: in the Verification state: computing the thresholds adaptively in a second way; accumulating and computing at least one statistics based on sets of selected significant local peaks and related characteristics in at least one adjacent sliding time window for verification of the detected event that is detected preliminarily; staying in the Verification state while the at least one statistics is being accumulated until sufficient statistics is collected for the verification; verifying the preliminarily detected event based on the thresholds and the at least one statistics; transitioning from the Verification state to the PeakFound state when the preliminarily detected event is concluded as “verified;” transitioning from the Verification state to the INIT state when verification is concluded to be “not verified;” and staying in the Verification state when verification is not concluded.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). A breathing rate estimator selects a subset of these peaks based on criteria to compute current motion characteristics. It also detects events related to motion or interference. The system further includes a system state controller that adaptively computes various parameters, including decision thresholds, T1, T2, and frequency bounds, all guided by a finite state machine (FSM) comprising INIT, Verification, PeakFound, and Motion states. In the INIT state, the controller detects events or interference, transitioning to Verification if an event is preliminarily detected. In the Verification state, thresholds are adaptively computed using a second method. The controller accumulates statistics from selected peaks over adjacent time windows to verify the preliminary event. It stays in Verification until enough statistics are gathered. If the event is verified, it transitions to the PeakFound state; if not verified, it returns to INIT; otherwise, it remains in Verification.
18. The system of claim 17 , wherein the system state controller is further configured for: in the PeakFound state: computing the thresholds adaptively in a third way; detecting the verified event based on the thresholds and the at least one of: the set of selected significant local peaks and related characteristics; detecting the excessive background interfering motion based on the thresholds and the remaining spectral energy; staying in the PeakFound state when the verified event is concluded as “detected;” transitioning from the PeakFound state to the INIT state when the verified event is concluded as “not detected” for a number of time instances; and transitioning from the PeakFound state to the Motion state when the excessive background interfering motion is concluded as “detected” for a number of time instances.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). A breathing rate estimator selects a subset of these peaks based on criteria to compute current motion characteristics. It also detects events related to motion or interference. The system further includes a system state controller that adaptively computes various parameters, including decision thresholds, T1, T2, and frequency bounds, all guided by a finite state machine (FSM) comprising INIT, Verification, PeakFound, and Motion states. Transitions occur based on detected events or interference. After successful verification in the Verification state, the system moves to the PeakFound state. Here, thresholds are adaptively computed using a third method. The controller detects the verified event and excessive background interfering motion based on these thresholds and system data. It stays in PeakFound if the event is continuously detected. If the verified event is not detected for several instances, it transitions to INIT. If excessive background interference is detected for multiple instances, it transitions to the Motion state.
19. The system of claim 16 , wherein the system state controller is further configured for: in the Motion state: computing the thresholds adaptively in a fourth way; detecting the excessive background interfering motion based on the thresholds and the remaining spectral energy; staying in the Motion state when the excessive background interfering motion is concluded as “detected;” and transitioning from the Motion state to the INIT state when the excessive background interfering motion is concluded as “not detected” for a number of time instances.
This system, which monitors object motion by analyzing wireless channel changes (TSCI) through transmitters and receivers, computes motion characteristics (like frequency) using a vital sign estimator. This estimator processes the time-series channel data, potentially involving preprocessing frequency components, converting to time domain coefficients, correcting timestamps, then bandpass filtering and frequency transforming these coefficients. It includes an energy spectrum normalizer and performs operations like filtering, machine learning, and weighted averaging across multiple antenna links and time domain coefficients. Further, a peak detector identifies significant local maxima in the frequency domain based on SNR-like parameters or amplitude thresholds (T1, T2). A breathing rate estimator selects a subset of these peaks based on criteria to compute current motion characteristics. It also detects events related to motion or interference. The system further includes a system state controller that adaptively computes various parameters, including decision thresholds, T1, T2, and frequency bounds, all guided by a finite state machine (FSM) comprising INIT, Verification, PeakFound, and Motion states. In the INIT state, the controller detects events or interference, transitioning to the Motion state if excessive background interfering motion is detected. In the Motion state, thresholds are adaptively computed using a fourth method. The controller continuously detects excessive background interfering motion based on these thresholds and the remaining spectral energy. It remains in the Motion state if this interference continues to be detected. If excessive background interfering motion is not detected for a specified number of instances, the system transitions back to the INIT state.
20. The system of claim 1 , wherein the vital sign estimator further comprises a spectral analyzer configured for: determining a timestamp associated with each channel information of the at least one TSCI; correcting timestamps of all channel information of a particular portion of the at least one TSCI in the current sliding time window so that the corrected timestamps of time-corrected channel information are uniformly spaced in time; and performing an operation on the time-corrected channel information with respect to the corrected timestamps, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on an output of the operation, wherein the operation comprises at least one of: preprocessing, processing, post-processing, filtering, linear filtering, nonlinear filtering, low-pass filtering, bandpass filtering, high-pass filtering, matched filtering, enhancement; restoration, de-noising, spectral analysis, frequency transform, inverse frequency transform, linear transform, nonlinear transform, feature extraction, machine learning, recognition, labeling, training, clustering, grouping, sorting, thresholding, peak detection, comparison with time-corrected channel information of another portion of another time series of channel information in another sliding time window, similarity score computation, vector quantization, compression, encryption, coding, storing, transmitting, representing, merging, combining, fusion, linear combination, nonlinear combination, and splitting.
A system monitors an object's pseudo-periodic motion in a venue. It uses a transmitter and receiver to detect channel changes caused by motion, extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating motion characteristics based on current or past channel data or prior characteristics. The vital sign estimator includes a spectral analyzer configured to determine a timestamp for each channel information instance and then correct all timestamps within a specific portion of the current time series to be uniformly spaced. It then performs various operations on this time-corrected channel information, which can include preprocessing, filtering (e.g., low-pass, bandpass), enhancement, de-noising, spectral analysis, frequency transforms, machine learning (e.g., feature extraction, recognition), thresholding, peak detection, comparison, or data merging and combining. The current characteristics related to the pseudo-periodic motion are then computed based on the output of this operation.
21. The system of claim 1 , wherein the vital sign estimator is further configured for: making the current characteristics related to the pseudo-periodic motion of the object available in real time; and moving the current sliding time window by a shift-size as time progresses.
A system monitors an object's pseudo-periodic motion in a venue. It uses a transmitter and receiver to detect channel changes caused by motion, extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating motion characteristics based on current or past channel data or prior characteristics. Additionally, the vital sign estimator is configured to make these current motion characteristics available in real-time. To ensure continuous monitoring, the system continually shifts the "current sliding time window" forward by a defined shift-size as time progresses, allowing for ongoing analysis.
22. The system of claim 1 , further comprising: an additional transmitter located at a third position in the venue and configured for transmitting a third wireless signal through the wireless multipath channel impacted by the pseudo-periodic motion of the object in the venue; an additional receiver located at a fourth position in the venue and configured for: receiving a fourth wireless signal through the wireless multipath channel, wherein the fourth wireless signal is determined based on both the wireless multipath channel and the third wireless signal, and obtaining an additional TSCI of the wireless multipath channel based on the fourth wireless signal, wherein the vital sign estimator is further configured for: determining that at least one portion of the additional TSCI in the current sliding time window is associated with the pseudo-periodic motion of the object in the venue, and computing the current characteristics related to the pseudo-periodic motion of the object in the current sliding time window based on at least one of: the at least one portion of the additional TSCI in the current sliding time window, at least one portion of the additional TSCI in an additional past sliding time window, and a past characteristics related to the pseudo-periodic motion of the object in the additional past sliding time window.
A system monitors an object's pseudo-periodic motion in a venue. It uses a transmitter and receiver to detect wireless channel changes caused by motion, extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating motion characteristics based on current or past channel data or prior characteristics. The system can further include an additional transmitter and receiver pair, also at different positions, transmitting and receiving wireless signals through the same motion-impacted channel to obtain an additional time series of channel information. The vital sign estimator is configured to determine if portions of this additional time series in the current window are associated with the object's motion. It then computes the current motion characteristics using these relevant portions of the additional time series, potentially alongside past additional channel data or past characteristics derived from that additional data.
23. The system of claim 1 , wherein the vital sign estimator is further configured for: determining that at least one portion of the at least one TSCI in the current sliding time window is associated with an additional pseudo-periodic motion of an additional object in the venue, wherein the wireless multipath channel is further impacted in the current sliding time window by the additional pseudo-periodic motion of the additional object; and computing a current characteristics related to the additional pseudo-periodic motion of the additional object in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, the at least one portion of the at least one TSCI in the past sliding time window, and a past characteristics related to the additional pseudo-periodic motion of the additional object in the past sliding time window.
A system monitors an object's pseudo-periodic motion in a venue, using a transmitter and receiver to detect wireless channel changes caused by motion and extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating motion characteristics based on current/past channel data or prior characteristics. Furthermore, the vital sign estimator can detect multiple motions simultaneously. It determines if parts of the channel information in the current window are associated with an additional pseudo-periodic motion from an additional object, acknowledging the channel is impacted by both. It then computes current characteristics for this additional object's motion, using channel data from the current window, past windows, or past characteristics specific to that additional motion.
24. The system of claim 23 , wherein the vital sign estimator is further configured for: determining that the wireless multipath channel is impacted in the current sliding time window by pseudo-periodic motions of a plurality of objects; computing current characteristics related to pseudo-periodic motions of the plurality of objects in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, the at least one portion of the at least one TSCI in the past sliding time window, and a past characteristics related to the pseudo-periodic motions of the plurality of objects in the past sliding time window; and estimating a quantity of the plurality of objects based on the current characteristics.
A system monitors an object's pseudo-periodic motion in a venue, using a transmitter and receiver to detect wireless channel changes caused by motion and extracting time-series channel information (e.g., CSI, CIR). A vital sign estimator identifies motion-related portions of this data in a current time window, calculating motion characteristics based on current/past channel data or prior characteristics. Furthermore, the vital sign estimator can detect multiple motions simultaneously. It determines if parts of the channel information in the current window are associated with an additional pseudo-periodic motion from an additional object, acknowledging the channel is impacted by both. It then computes current characteristics for this additional object's motion, using channel data from the current window, past windows, or past characteristics specific to that additional motion. Building on this, the vital sign estimator is further configured to determine if the wireless channel is impacted by pseudo-periodic motions from a plurality of objects. It then computes current characteristics for the motions of all these objects and uses these characteristics to estimate the total quantity of objects present in the venue.
25. A method for monitoring object motion in a venue, comprising: receiving a wireless signal as an output of a wireless multipath channel, wherein the wireless multipath channel is impacted by a pseudo-periodic motion of an object in the venue; extracting at least one time series of channel information (CI) of the wireless multipath channel from the wireless signal, wherein each CI comprises at least one of: a channel state information (CSI), a frequency domain CSI, a frequency domain CSI associated with at least one sub-band, a time domain CSI, a channel impulse response (CIR), a channel frequency response (CFR), a channel characteristics, and a channel filter response, of the wireless multipath channel; determining that at least one portion of the at least one time series of CI (TSCI) in a current sliding time window is associated with the pseudo-periodic motion of the object in the venue; and computing a current characteristics related to the pseudo-periodic motion of the object in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, at least one portion of the at least one TSCI in a past sliding time window, and a past characteristics related to the pseudo-periodic motion of the object in the past sliding time window.
A method for monitoring an object's pseudo-periodic motion in a venue involves receiving a wireless signal, which is an output of a wireless multipath channel affected by the object's motion. From this received wireless signal, at least one time series of channel information (TSCI), which can include Channel State Information (CSI) or Channel Impulse Response (CIR), is extracted. The method then determines that at least one portion of this time series of channel information within a current sliding time window is associated with the pseudo-periodic motion of the object. Finally, it computes current characteristics related to this pseudo-periodic motion in the current sliding time window. This computation is based on channel information from the current window, from a past sliding time window, or on previously determined past characteristics of the motion.
26. The method of claim 25 , further comprising: processing the at least one TSCI in the current sliding time window on both time domain and frequency domain; and detecting at least one peak in the frequency domain, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on the at least one peak in the frequency domain, wherein at least one of the current characteristics and the past characteristics comprises information related to at least one of: a frequency of pseudo-periodic motion, a frequency characteristics, a frequency spectrum, a time period of pseudo periodic motion, a temporal characteristics, a temporal profile, a timing of pseudo-periodic motion, a starting time, an ending time, a duration, a history of motion, a motion type, a motion classification, a location of the object, a speed, a displacement, an acceleration, a rotational speed, a rotational characteristics, a gait cycle of the object, a transient behavior of the object, a transient motion, a change in pseudo-periodic motion, a change in frequency of pseudo-periodic motion, a change in gait cycle, an event associated with pseudo-periodic motion, an event associated with transient motion, a sudden-motion event, and a fall-down event.
A method for monitoring an object's pseudo-periodic motion in a venue involves receiving a wireless signal, which is an output of a wireless multipath channel affected by the object's motion. From this received wireless signal, at least one time series of channel information (TSCI), which can include Channel State Information (CSI) or Channel Impulse Response (CIR), is extracted. The method then determines that at least one portion of this time series of channel information within a current sliding time window is associated with the pseudo-periodic motion of the object. Finally, it computes current characteristics related to this pseudo-periodic motion in the current sliding time window. This computation is based on channel information from the current window, from a past sliding time window, or on previously determined past characteristics of the motion. The method further involves processing the time-series channel information in the current window in both time and frequency domains, and detecting at least one peak in the frequency domain. The current motion characteristics are then computed based on these detected frequency peaks. These characteristics can include detailed information such as motion frequency, time period, duration, motion history, type, location, speed, acceleration, rotational aspects, gait cycle, transient behaviors, changes in motion, or specific events like sudden movements or falls.
27. The method of claim 25 , further comprising: determining a timestamp associated with each channel information of the at least one TSCI; correcting timestamps of all channel information of a particular portion of the at least one TSCI in the current sliding time window so that the corrected timestamps of time-corrected channel information are uniformly spaced in time; and performing an operation on the time-corrected channel information with respect to the corrected timestamps, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on an output of the operation, wherein the operation comprises at least one of: preprocessing, processing, post-processing, filtering, linear filtering, nonlinear filtering, low-pass filtering, bandpass filtering, high-pass filtering, matched filtering, enhancement, restoration, de-noising, spectral analysis, frequency transform, inverse frequency transform, linear transform, nonlinear transform, feature extraction, machine learning, recognition, labeling, training, clustering, grouping, sorting, thresholding, peak detection, comparison with time-corrected channel information of another portion of another time series of channel information in another sliding time window, similarity score computation, vector quantization, compression, encryption, coding, storing, transmitting, representing, merging, combining, fusion, linear combination, nonlinear combination, and splitting.
A method for monitoring an object's pseudo-periodic motion in a venue involves receiving a wireless signal, which is an output of a wireless multipath channel affected by the object's motion. From this received wireless signal, at least one time series of channel information (TSCI), which can include Channel State Information (CSI) or Channel Impulse Response (CIR), is extracted. The method then determines that at least one portion of this time series of channel information within a current sliding time window is associated with the pseudo-periodic motion of the object. Finally, it computes current characteristics related to this pseudo-periodic motion in the current sliding time window. This computation is based on channel information from the current window, from a past sliding time window, or on previously determined past characteristics of the motion. The method further includes determining a timestamp for each piece of channel information and correcting all timestamps within a specific portion of the current time series to be uniformly spaced. Various operations are then performed on this time-corrected channel information, such as preprocessing, filtering, de-noising, spectral analysis, frequency transforms, machine learning, peak detection, comparison, or data merging. The current characteristics are then computed based on the output of this operation.
28. A receiver of a motion monitoring system, comprising: a wireless circuitry configured to receive a wireless signal as an output of a wireless multipath channel, wherein the wireless multipath channel is impacted by a pseudo-periodic motion of an object in a venue, wherein an input signal of the wireless multipath channel is transmitted asynchronously by a transmitter of the motion monitoring system, wherein the transmitter is different from the receiver and different from the object; a processor communicatively coupled with the wireless circuitry; a memory communicatively coupled with the processor; and a set of instructions stored in the memory which, when executed, causes the processor to extract at least one time series of channel information (CO of the wireless multi path channel from the wireless signal, wherein: at least one portion of the at least one time series of CI (TSCI) in a current sliding time window is associated with the pseudo-periodic motion of the object in the venue, and the at least one portion of the at least one TSCI in the current sliding time window is to be used by a vital sign estimator of the motion monitoring system to compute a current characteristics related to the pseudo-periodic motion of the object in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, at least one portion of the at least one TSCI in a past sliding time window, and a past characteristics related to the pseudo-periodic motion of the object in the past sliding time window.
A receiver for a motion monitoring system comprises wireless circuitry that receives a wireless signal. This signal is an output of a wireless multipath channel, which is impacted by the pseudo-periodic motion of an object in a venue. Notably, the input signal for this channel is transmitted asynchronously by a separate transmitter, distinct from both the receiver and the object. The receiver also includes a processor and memory storing instructions. When executed, these instructions cause the processor to extract at least one time series of channel information (TSCI) from the received wireless signal. A crucial function is to identify portions of this time series within a current sliding time window that are associated with the object's pseudo-periodic motion. These identified portions of the time series are then intended to be used by a vital sign estimator (another part of the motion monitoring system) to compute current characteristics related to the object's motion, drawing upon current or past channel data, or prior motion characteristics.
29. The receiver of claim 28 , wherein the vital sign estimator is coupled to the receiver and configured for: processing the at least one TSCI in the current sliding time window on both time domain and frequency domain; and detecting at least one peak in the frequency domain, wherein the current characteristics related to the pseudo-periodic motion of the object is computed based on the at least one peak in the frequency domain, wherein at least one of the current characteristics and the past characteristics comprises information related to at least one of: a frequency of pseudo-periodic motion, a frequency characteristics, a frequency spectrum, a time period of pseudo periodic motion, a temporal characteristics, a temporal profile, a timing of pseudo-periodic motion, a starting time, an ending time, a duration, a history of motion, a motion type, a motion classification, a location of the object, a speed, a displacement, an acceleration, a rotational speed, a rotational characteristics, a gait cycle of the object, a transient behavior of the object, a transient motion, a change in pseudo-periodic motion, a change in frequency of pseudo-periodic motion, a change in gait cycle, an event associated with pseudo-periodic motion, an event associated with transient motion, a sudden-motion event, and a fall-down event.
A receiver for a motion monitoring system comprises wireless circuitry that receives a wireless signal, which is an output of a wireless multipath channel impacted by an object's pseudo-periodic motion in a venue, transmitted asynchronously by a separate transmitter. It includes a processor and memory with instructions to extract time series channel information (TSCI) from the signal, identifying motion-related portions in a current time window. These portions are intended for a vital sign estimator to compute current motion characteristics based on current or past channel data or prior characteristics. In this setup, the vital sign estimator is coupled to the receiver. It is configured to process the extracted time series channel information in the current window across both time and frequency domains, and to detect at least one peak in the frequency domain. The current motion characteristics are computed based on these detected frequency peaks. These characteristics can encompass comprehensive information such as motion frequency, time period, duration, history, type, location, speed, acceleration, rotational data, gait cycle, transient behaviors, changes in motion, or specific events like sudden movements or falls.
30. An estimator of a motion monitoring system, comprising: a processor; a memory communicatively coupled with the processor; and a set of instructions stored in the memory which, when executed, causes the processor to perform: obtaining at least one time series of channel information (TSCI) of a wireless multipath channel extracted by a receiver of the motion monitoring system from a wireless signal received as an output of the wireless multipath channel, wherein the wireless multipath channel is impacted by a pseudo-periodic motion of an object in a venue, and wherein an input signal of the wireless multipath channel is transmitted by a transmitter of the motion monitoring system, wherein the transmitter is different from the receiver and different from the object, determining that at least one portion of the at least one TSCI in a current sliding time window is associated with the pseudo-periodic motion of the object in the venue, and computing a current characteristics related to the pseudo-periodic motion of the object in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, at least one portion of the at least one TSCI in a past sliding time window, and a past characteristics related to the pseudo-periodic motion of the object in the past sliding time window.
An estimator within a motion monitoring system comprises a processor and memory storing instructions. When executed, these instructions enable the processor to obtain at least one time series of channel information (TSCI). This TSCI was previously extracted by a receiver of the motion monitoring system from a wireless signal, which itself was received as an output of a wireless multipath channel. This channel is specifically impacted by the pseudo-periodic motion of an object in a venue, and the channel's input signal was transmitted by a transmitter that is distinct from both the receiver and the object. The estimator then determines that at least one portion of this obtained TSCI in a current sliding time window is associated with the pseudo-periodic motion of the object. Finally, it computes current characteristics related to this pseudo-periodic motion in the current sliding time window, basing its calculation on channel information from the current window, from a past sliding time window, or on previously determined past characteristics of the motion.
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August 4, 2020
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